Non-white noise in fMRI: Does modelling have an impact?

Lund, Torben Ellegaard; Madsen, Kristoffer Hougaard; Sidaros, Karam; Luo, Wen‐Lin; Nichols, Thomas E.

doi:10.1016/j.neuroimage.2005.07.005

Cited by 388 publications

(352 citation statements)

References 26 publications

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“…Spectral Signature of Regional BOLD Activity Previous studies using either blind source separation (Beckmann and Smith 2004) or nuisance variable regression (Lund et al 2006) have shown that a significant portion of resting-state fMRI signals can be attributed to nonneuronal fluctuations due to slow scanner drift, head motion, and respiratory and cardiac cycles ( Bianciardi et al 2009). In this study, we presented a technique to further separate and to quantify neurogenic contributions from various spectral components of neuroelectrical activity observed with concurrently acquired EEG.…”

Section: Discussionmentioning

confidence: 99%

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Li¹,

Zwart²,

Chang³

et al. 2013

Cerebral Cortex

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Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity.

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Section: Discussionmentioning

confidence: 99%

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Li¹,

Zwart²,

Chang³

et al. 2013

Cerebral Cortex

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“…To correct for the structured noise induced by respiration and cardiac pulsation, we included RETROICOR (RETROspective Image-based CORrection method) nuisance covariates in the design matrix (49). We also included 24 regressors to remove residual movement artifacts with spin history effects (50,51). Linear contrasts were used to test the effects of interests: route recognition vs. random dots.…”

Section: Methodsmentioning

confidence: 99%

Neural correlates of virtual route recognition in congenital blindness

Kupers

Chebat

Madsen

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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131

133

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Despite the importance of vision for spatial navigation, blind subjects retain the ability to represent spatial information and to move independently in space to localize and reach targets. However, the neural correlates of navigation in subjects lacking vision remain elusive. We therefore used functional MRI (fMRI) to explore the cortical network underlying successful navigation in blind subjects. We first trained congenitally blind and blindfolded sighted control subjects to perform a virtual navigation task with the tongue display unit (TDU), a tactile-to-vision sensory substitution device that translates a visual image into electrotactile stimulation applied to the tongue. After training, participants repeated the navigation task during fMRI. Although both groups successfully learned to use the TDU in the virtual navigation task, the brain activation patterns showed substantial differences. Blind but not blindfolded sighted control subjects activated the parahippocampus and visual cortex during navigation, areas that are recruited during topographical learning and spatial representation in sighted subjects. When the navigation task was performed under full vision in a second group of sighted participants, the activation pattern strongly resembled the one obtained in the blind when using the TDU. This suggests that in the absence of vision, cross-modal plasticity permits the recruitment of the same cortical network used for spatial navigation tasks in sighted subjects.cross-modal plasticity | parahippocampus | sensory substitution | spatial navigation | visual cortex T he ability to navigate efficiently in large-scale environments was always a predicate for human survival, now applied to the particular challenges of living in a modern, urban society. Visual cues signaling the location of landmarks play a key role in facilitating the formation of spatial cognitive maps used for path finding in a visual setting (1, 2). Despite the importance of vision in spatial cognition, the abilities to recognize a traveled route and to represent spatial information are maintained in blind individuals (3-5), probably through tactile, auditory, and olfactory cues, as well as motion-related cues arising from the vestibular and proprioceptive systems.During successful navigation, spatial information needs to be encoded and retrieved. The role of the hippocampus for navigation in large-scale environments has been amply demonstrated in both animal (6-8) and human studies (9-11). Besides the hippocampus, several other areas in the posterior mesial lobe and posterior parietal, occipital, and infero-temporal cortices also play an important role in navigation (9,(12)(13)(14)(15)(16)(17).The neural correlates of navigation in congenital blindness remain elusive, in part owing to the difficulty in testing navigational skills of blind subjects within the setting of a functional brain imaging study. To circumvent this difficulty, we trained blind and sighted subjects in a spatial navigation task using the tongue display unit (TDU), a vis...

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“…− 1 ((t − Δt)/τ) exp(− (t − Δt)/τ) for t ≥ Δt and 0 otherwise, with τ = 1.2, n = 3 and Δt = 1 (Boynton et al 1996). As nuisance regressors, we used a constant term, a linear term, and a discrete cosine set implemented in SPM2 with a minimum period of 120 seconds (Worsley and Friston 1995;Lund et al 2006). For statistical inference with RETROICOR, physiological noise regressors obtained from the cardiac and respiratory measurements were used to form the physiological noise matrix P (Glover et al 2000b;Restom et al 2006).…”

Section: General Linear Model Analysismentioning

confidence: 99%

“…More recently, RETROICOR has been extended to a general linear model (GLM) framework (Lund et al 2006) and modified for use in ASL studies (Restom et al 2006). A recent adaptation for BOLD based imaging employs additional regressors describing variations in respiratory volume (Birn et al 2006).…”

Section: Introductionmentioning

confidence: 99%

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

et al. 2007

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A component based method (CompCor) for the reduction of noise in both blood oxygenation level dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regionsof-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time-series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.

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Non-white noise in fMRI: Does modelling have an impact?

Cited by 388 publications

References 26 publications

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Neural correlates of virtual route recognition in congenital blindness

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

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